Forest Harvest Levels in Minnesota

Forest Harvest Levels in Minnesota

Effects of Selected Forest Management Practices

on Sustained Timber Yields

Christopher R Schwalm

Research Scientist

Technical Report

Minnesota Department of Natural Resources

Original April 25, 2008

Last Revision July 10, 2009

Acknowlegements

The author gratefully acknowledges assistance and guidance of the Forest Harvest Level

Analysis Advisory Group.

Members: Minnesota DNR Dave Epperly - Director, Forestry Division

Dave Schad - Director, Fish & Wildlife Division

Steve Hirsch - Director, Ecological Resources Division

Christopher Schwalm - Forest Research Scientist

Keith Jacobson - Program Supervisor

University of Minnesota College of Food, Agricultural and Natural Resource Sciences Alan Ek - Professor and Department Head, Department of Forest Resources

Howard Hoganson - Professor, Department of Forest Resources

United States Department of Agriculture - Forest Service Rob Harper - Supervisor, Chippewa National Forest

Mike Prouty - Field Representative, St. Paul Field Office, State & Private Forestry

Tom Schmidt - Assistant Director, Northern Research Station

Minnesota Association of County Land Commissioners

Robert Krepps, Past Chair

University of Minnesota Duluth Natural Resources Research Institute Mike Lalich – Director

Minnesota Forest Industries Tim O’Hara - Vice President for Forest Policy

i

Table of Contents

Executive Summary ........................................................................................ iii

Purpose ......................................................................................................... iii

Methods ........................................................................................................ iii

Results .......................................................................................................... iv

Conclusions .................................................................................................. v

Introduction ..................................................................................................... 1

Methods ............................................................................................................ 1

Baseline Scenario ......................................................................................... 2

Maximum Biological Growth ...................................................................... 6

Alternative Scenario Development .............................................................. 6

Results and Discussion .................................................................................... 7

Comparison of Baseline Scenario to Current Practices and

FIA-based Growth Estimates ....................................................................... 7

Changes in Timber Outputs by Scenario ..................................................... 8

Caveats ......................................................................................................... 11

Need for Additional Analysis ...................................................................... 12

References ........................................................................................................ 12

Tables ............................................................................................................... 14 Table 1. Type mappings and areal extent of timberland based on 2005

FIA data .................................................................................................. 14

Table 2. Ownership classes and areal extent of timberland based on

2005 FIA data ......................................................................................... 15

Table 3. Cover type mappings used for logging residue ............................. 15

Table 4. Name and parameter grid for all 16 scenarios. A dashed entry

indicates the absence of that constraint or management practice ........... 16

Table 5. Scenario parameter key ................................................................. 17

Table 6. Volume harvested in the baseline scenario compared to

published estimates ................................................................................. 18

Table 7. FIA-derived estimates of growth, mortality, and maximum

biological growth contrasted with ranges of prescriptive scenarios

and current harvest levels. All FIA-based estimates are averages

from the 2004-2007 Minnesota annual inventories and reference all

live trees on forest land ........................................................................... 18

ii

Table 8. Changes in sustained timber yield by ownership relative to

current practices estimated by altering selected management

practices and policies singly. A dashed entry indicates no data.

statewide totals may differ due to rounding ........................................... 19

Figures .............................................................................................................. 20

Figure 1. Age class distribution of the aspen cover type (including

balsam poplar; total area = 5.3 mil ac) ................................................... 20

Figure 2. Age class distribution of the balsam fir cover type (total

area = 0.4 mil ac) .................................................................................... 21

Figure 3. Age class distribution of the b lack spruce cover type

(total area = 1.3 mil ac) ........................................................................... 22

Figure 4. Age class distribution of the jack pine cover type (total

area = 0.4 mil ac) .................................................................................... 23

Figure 5. Age class distribution of the paper birch cover type

(total area = 1 mil ac) .............................................................................. 24

Figure 6. Age class distribution of the red pine cover type

(total area = 0.4 mil ac) ........................................................................... 25

Figure 7. Age class distribution of the tamarack cover type

(total area = 0.9 mil ac) ........................................................................... 26

Figure 8. Age class distribution of the white spruce cover type

(total area = 0.1 mil ac) ........................................................................... 27

Appendix A ...................................................................................................... 28 Table 1A. Rotation ages and effective ERF percentages for

even-aged types ....................................................................................... 28

Table 2A. Operability criteria for uneven aged-types ................................. 29

Table 3A. Operability criteria for types where thinning were allowed ....... 29

Table 4A. Harvested volume by ownership and areal extent of

management by treatment type for all scenarios .................................... 30

Table 5A. Harvested volume by species for all scenarios ........................... 31

Appendix B: Additional Scenario .................................................................. 32

Appendix C: Summary Document ................................................................ 35

iii

Executive Summary

Purpose One key recommendation of the 2007 Governor’s Task Force on the Competitiveness of

Minnesota’s Primary Forest Products Industry was to explore the feasibility of increasing total

statewide timber production across all ownerships to 5.5 million cords per year (mil cd yr-1

) in an

environmentally sustainable manner. Statewide harvest levels have averaged approximately 3.69

mil cd yr-1

over the past ten years, thus achieving the task force recommendation might require a

significant change in land management practices. One objective of this analysis is to determine if

a sustained timber yield of 5.5 mil cd yr-1

is achievable and to quantify changes in management

practices that could potentially increase utilization toward this level. This study starts from the

existing state of forest management as well as biologically maximum growth and then explores

potential forest management scenarios between these two endpoints using model and data-based

approaches.

This analysis was done in order to provide information to policymakers, forest managers, and

proposers of new industrial facilities to help assess future timber yields and forest age classes

under a range of potential management and policy options that might help increase statewide

harvest levels. The focus is largely on resource analysis, i.e., potential yields rather than social,

environmental or economic, and as such the study is far more limited in scope than the 1994

Generic Environmental Impact Statement on Timber Harvesting (GEIS). However, the study did

incorporate the Minnesota Forest Resources Council guidelines, reserved forest areas,

consideration of old growth, etc. Thus aspects of various social, economic, and environmental

considerations were incorporated.

Methods An Advisory Group formed by the State Forester provided assistance through peer review and

advice during preparation of the analysis and report. The Advisory Group also made

recommendations on future analysis needs beyond the scope of this original work.

A state-of-the-art forest estate modeling framework (Remsoft, Fredericton, NB, Canada) was

used to model sustainable harvest levels across all ownerships in Minnesota. The modeled land

base was derived from Minnesota 2005 Forest Inventory and Analysis (FIA) data. Only

nonreserve, productive, stocked timberland was included, resulting in 14,743,269 acres

represented by 6,331 field plots. Each plot was placed into a development type: a unique

combination of five-year age class, forest type, site productivity class, and ownership class. Each

development type had its own parameterization, e.g., rotation ages, extent of extended rotation

forestry, and timber yield stream, and was projected 50 years into the future using ten five-year

planning periods. Development types were assigned one of four generic management activities

(even-aged vs. uneven-aged management, commercial thinning, or no entry). The resulting

schedule represented the highest sustainable harvest level over the 50-year period subject to

constraints that reflect how timberlands are currently managed. This baseline harvest scenario

was then augmented by alternative scenarios that explored changes in sustainable harvest levels

relative to changes in forest management practices. In conjunction with these scenarios, FIA-

based estimates of growth were used to determine if the forest land base can supply enough

roundwood to meet the key recommendation. The effect of management practices on sustainable

iv

timber yield levels was quantified by modeling alternative management practices singly as

follows: (1) addressing market and process constraints or inefficiencies in current practices (e.g.,

insuring that, within no longer than five years, all tracts identified for management are fully

operationalized, from setup through to scaling), (2) reducing the amount of effective extended

rotation forestry on public lands, (3) allowing federal lands to be managed for timber

productivity, (4) assuming full cross-ownership cooperation, (5) removing operability concerns

in white cedar and black ash types, (6) placing less emphasis on regulated age class distributions,

(7) adopting more aggressive thinning regimes, (8) allowing for intensive management to restore

full productivity, (9) reducing rotation ages for even-aged systems, (10) augmenting the land

base (increased access to private lands), and (11) utilization of logging residue.

The focus of this analysis was to examine harvest-level impacts of various management and

policy options. Harvest-level impacts are only one important consideration when examining

forest management and policy options and the analysis is not designed to explicitly examine the

social or environmental desirability of these options.

Results Under current practices the sustainable harvest level was capped at the 1996-2005 average of

3.69 mil cd yr-1

. Most roundwood volume was generated in even-aged systems at final harvest,

87% by volume and 81% by area treated. The aspen type played a pivotal role: 69% of the

statewide total harvested volume and 59% of all harvest activity by area was in the aggregated

aspen type. Overall yield, total volume harvest divided by total acres treated, was 19.5 cd ac-1

.

FIA-based estimates of growth exceeded current practices and the recommended 5.5 mil cd yr-1

target, i.e., maximum biological growth (12.56 mil cd yr-1

) > gross growth (9.56 mil cd yr-1

) >

net growth (5.00 mil cd yr-1

) > current harvest levels. Mortality based on fire, animal damage,

insect and disease, and overstocking totaled 1.04 mil cd yr-1

in 2005. These estimates of gross

and net growth also suggest that capture of a greater portion of mortality has the potential to

increase net growth and thus sustainable harvest levels. While complete capture is not possible, it

is notable that capture of 40% would increase sustainable harvest levels by 1.8 mil cd yr-1

and

would satisfy the 5.5 mil cd yr-1

recommendation.

The inclusion of logging residue (increased utilization) in the context of working biomass

markets would increase effective harvest levels by 640,000 cd yr-1

. In terms of more traditional

roundwood silviculture, changes in forest management practice that led to significant increases

(≥10% change) in sustainable harvest levels were: (1) addressing market and process constraints,

e.g., ensuring that all stands selected for treatment result in harvested and scaled volume within

one planning period or, ideally, one year (+509,000 cd yr-1

); (2) more aggressive and intensive

thinning regimes (+478,000 cd yr-1

); and (3) the use of timber stand improvements, i.e.,

precommercial activities designed to allow a stand to reach its full biological productivity

(+520,000 cd yr-1

). When applied in concert these changes alone could augment sustainable

harvest levels by 2.15 mil cd yr-1

thereby increasing harvest by 58% to 5.84 mil cd yr-1

over the

50-year planning horizon.

The main scenarios (Tables 4-5 & 8) quantify the change in harvest level relative to changing

one management policy. Beyond this, one additional scenario was added at the request of several

Advisory Group members to address a desire for analysis of a single harvest or timber yield

v

level, overall and by species that would be achievable given several altered constraints. To

estimate these harvest levels an additional scenario was designed that removed ―market‖

constraints, eased even-flow constraints on several forest types, and altered three policies or

practices at once. A timber yield of approximately 6 million cords annually was found to be

achievable under these constraints and conditions. This is not a ―recommended‖ harvest level,

but is instead an examination of timber yields and age-class impacts given the scenario

parameters and constraints (see Appendix B for details).

Conclusions In conclusion, the current harvest level has showed little variation for the 1996-2005 period and

falls short of FIA-based estimates of maximum, gross, and net growth. Improved markets and

processes, more frequent thinnings, removing greater volume (i.e., utilization of

nonmerchantable stems as biomass) with each entry, the use of precommercial forest

management activities (e.g., seedbed preparation, fertilization, removing overtopped specimens)

to restore full productivity, and increased utilization (particularly in the form of logging residue)

all have potential to sustainably meet and exceed the 5.5 mil cd yr-1

harvest level

recommendation on timberlands in Minnesota. While decisions on forest management policies

and/or and investment levels are outside the scope of this analysis, quantification of potential

harvest volume effects is one important factor that can be considered by decision makers, in

addition to environmental and social benefits.

This study should be seen as one important step in the continuing process of analyzing forest

management practices, policies and opportunities. Advisory Group recommendations for the

future include maintaining and expanding capacity to do a range of statewide analyses in a

timely manner, including regular harvest-level analyses updates. Additional analyses should be

expanded and integrated to address:

$ A wide range of important ecosystem services (water quality, wildlife habitat,

carbon sequestration, etc.)

$ Information and assumptions related to private lands timber availability

$ Economics, including high valued-added products, forest management investment

opportunities, transportation needs, and impacts of potential budget limitations

$ Opportunities for integrating/mitigating increased biofuels production with timber

production and ecosystem services

$ Potential utilization and marketing opportunities and likely impacts

1

Introduction

Contemporary forest resource management faces numerous challenges in Minnesota. These

range from parcelization and fragmentation of the forested land base, sluggish economic demand

for forest products, emerging biomass markets, and increased competitive pressure from

globalized markets. These challenges formed the backdrop for the 2007 Governor’s Task Force

on the Competitiveness of Minnesota’s Primary Forest Products Industry (MN DNR, 2007). One

key recommendation of this task force was to explore the feasibility of increasing total statewide

timber production across all ownerships to 5.5 mil cd yr-1

. Statewide harvest levels have

averaged 3.69 mil cd yr-1

over the 1996-2005 period, with little variation. This history suggests

that achieving this benchmark might require significant changes in land management practices.

Furthermore, any potential changes in current land management practices must be carefully

considered in the context of sustainability that references forest growth, timber production, and

nontimber values such as wildlife habitat.

This analysis was done in order to provide information to policymakers, forest managers and

proposers of new industrial facilities to help assess future timber yields and forest age classes

under a range of potential management and policy options that might help increase statewide

harvest levels. The focus is largely on resource analysis, i.e., potential yields rather than social,

environmental or economic, and as such the study is far more limited in scope than the 1994

Generic Environmental Impact Statement on Timber Harvesting (GEIS). However, the study did


consideration of old growth, etc. Thus aspects of various social, economic, and environmental


This analysis starts from the existing state of forest management and then estimates an upper

limit on timber productivity based on maximum biological growth. Management scenarios,

implemented in a forest estate model driven by linear optimization, are then used to explore

potential harvest levels between these two endpoints. The objectives of this analysis are fourfold:

(1) to develop a modeled baseline scenario of the forestry situation in Minnesota that mimics

current practices and outputs; (2) to determine, using alternative scenarios and ancillary data, if

the forested land base in Minnesota can provide a sustainable harvest level of 5.5 mil cd yr-1

; (3)

to quantify changes in harvest levels as a function of management practices and (4) to provide

harvest level volume impacts as one important part of informing decisions on potential changes

to current management regimes to achieve the key 5.5 mil cd yr-1

recommendation. Clearly,

policymakers will need to also consider environmental and social impacts of any management

changes.

Methods

An Advisory Group formed by the State Forester provided assistance through peer review and

advice during development of the analysis and report. The Advisory Group also made

recommendations for future analysis beyond the scope of this original work.

2

Baseline Scenario The modeled land base was drawn from the 2005 Forest Inventory and Analysis (FIA) data for

Minnesota. For this study the field plot level compilation of FIA data was used (Miles and Pugh,

2007). Only nonreserve, productive, stocked timberland (i.e., commercial forest) was included,

resulting in 14,743,269 acres represented by 6,331 field plots. Each plot was then placed into a

development type: a unique combination of five-year age class, forest type (Table 1), site

productivity class, and ownership (Table 2)1. Development type area was calculated using

volume-based expansion factor (VEF)2. Site productivity classes were bins of five site index unit

increments from 20 to 90.

While the use of FIA data enables analysis across the whole state, there are two important

caveats. First, FIA data is inherently aspatial. Each plot represents, on average, 2,000–3,000

acres and there is no clear sense of adjacency between plots or stand boundaries. This poses

problems for analyzing any aspect of forest management where spatiality is important, e.g.,

parcelization, fragmentation, and wildlife habitat. The latter was addressed in an aspatial manner

by tracking effective extended rotation forestry (EERF). This is a concept used in the forest

planning process on state-owned lands (MN DNR, n.d.) and refers to the portion of a type-

specific age class distribution where stands are purposely held past normal rotation ages to

facilitate nontimber values. This concept was applied as a threshold, i.e., a fixed percentage of

EERF by type was an explicit target. These thresholds were applied to state and federal lands

only on primarily even-aged systems (Appendix Table A1) and deal only with wildlife species

that may require later successional forest types, and age classes beyond biological or economic

rotation, as habitat.

The second caveat concerns ownership. In the 2005 FIA there are only four major categories

(Table 2) and private ownership lacks any differentiation between, for example, nonindustrial

private forest owners (NIPF), timber investment management organizations (TIMOS), real estate

investment trusts (REITS), or the other (typically held by vertically integrated firms) industrial

land base. As management regimes can be expected to vary among these subgroups the modeled

results for private ownership are aggregated and can only be sensibly interpreted as broad

averages. Furthermore, estimates of the percentage of total private land holdings available for

management activity vary. Birch (1996) reported that landowners representing 86% of private

land holdings, by area, were amenable to harvest activity either in the next decade or indefinitely.

The GEIS on Timber Harvesting and Forest Management in Minnesota (Jaakko Pöyry

Consulting, Inc., 1994) assumed 90% of private lands were open to management activity. The

UPM/Blandin Paper Thunderhawk Project (Thunderhawk) (Johnson et al., 2006) assumed that

1 An FIA inventory data error in assigning state/county ownership was discovered late in the process of preparing

this analysis and report. Approximately 272,820 acres classified as county land in 2003 data were erroneously

classified as state land in the 2005 data (6/23/09 personal communication, Pat Miles, FIA Analyst). Figures in this

study related to county and state ownerships should be adjusted accordingly.

2 FIA data offer two expansion factors: VEF, used to scale plot level volumes, and an area-based version (AEF),

used to scale plot level areas. As the key recommendation is formulated in terms of harvested volume VEF was

used by the model internally to characterize plot area. However, areal extent of harvest is also of interest so an

aggregated relationship between VEF and AEF across all plots was developed: 1 ac VEF = 0.75 ac AEF. This

was used to estimate areal extent of harvest for all development types.

3

the willingness to harvest on private land was linked to initial age class, cover type, and scenario.

However, despite this nuanced approach volumes harvested on private lands showed little

variability across scenarios with the exception of both HighAspen scenarios. Under these

scenarios there was a significant increase in harvest volumes on private lands, primarily a

function of removing a statewide volume cap in the modeling framework and not a function of

private land availability per se. For this study we assumed 90% of private lands, by area, were

available to be harvested, i.e., were operable with the modeling framework determining which

parcels were excluded relative to the objective function and management scenario. It is

noteworthy that this only applies to cover types without additional operability constraints, e.g.,

low productivity elm-ash-cottonwood (see below). On such types the more restrictive limits were

used. Growth and yield was invariant across all ownership subgroups and all model outputs

reference private ownership in the aggregate.

This analysis used three silvicultural systems: even-aged and uneven-aged management, and

thinning. The parameterization of each even-aged system varied by ownership (Appendix Table

A1). Uneven-aged management and thinning were invariant across all ownerships and assumed

that each thinning removed a fixed percentage (= 33%) of standing volume (Appendix Table A2

& A3). The northern white cedar, elm-ash-cottonwood (lowland hardwoods), and maple-birch

types (northern hardwoods) were further constrained by operability limits, the latter two solely

on poor sites (site index < 50). The areal extent of management activity in each of these types is

limited to a fixed percentage of the operable land base (1%, 10%, and 10% respectively) for this

study period. Similarly, the amount of harvested volume for tamarack species was capped at its

current utilization rate of 70,000 cd yr-1

. These limits represent current practices dictated by

silvicultural challenges in regeneration, lack of markets, forest health, and type-specific wildlife

habitat concerns (Jacobson, 2007).

Existing best management practices (BMPs), with emphasis on riparian areas and, where

applicable, seed trees were addressed through a single aggregated silvicultural parameter: each

stand was required to leave a fixed percentage (= 5%) of volume on site.

Growth and yield tables for development types (irrespective of ownership) were estimated at the

midpoint of each five-year age class using a modified version of Walters and Ek (1993; hereafter

WE93). The WE93 study detailed several equations for predicting gross volume metrics and

stand characteristics, e.g., gross volumes, quadratic mean diameter (QMD), at the stand level as

functions of stand type, site index, and stand age. WE93 was developed using 1977 FIA data

from the Aspen-Birch Unit of Minnesota. For this study the equations used in WE93 for basal

area (BA) and QMD, which serve as arguments for the volume equations, were recalibrated

using 2005 FIA data; the functional form was maintained but coefficients were re-estimated

using current FIA data. This was done to correct a large bias, due to changes in plot protocols

and typing procedures, when using the original WE93 equations on the 2005 FIA plot data.

Mean bias of gross merchantable volume, BA, and QMD in the aspen type was -387.02 ft3,

-13.59 ft2, and -0.79" respectively using the original WE93 and -174.06 ft

3, -0.13 ft

2, and 0" after

re-estimation based on the 2005 FIA. A scaling factor was also used (WE93, pg. 84) for each

type to correct for systematic over- or under-estimation of merchantable volumes. Finally,

merchantable gross volumes using WE93 were a function of a fixed stump height (1') and top

diameter outside bark (dob = 3").

4

Two enhancements of WE93 were also applied. First, it is known that stands, after some age and

barring human disturbance or calamity, begin to decline and ultimately transition to a different

type. This is generally associated with a loss of merchantable volume. However, WE93 volumes

are monotonically increasing functions of age. In order to add more realism to WE93 each

aggregated cover type was assigned a maximum rotation age (MRA) based on the Minnesota

Department of Natural Resources (MN DNR) internal forest planning procedures (MN DNR,

n.d.). After MRA stand volume was assumed to decline. To implement that decline, the volume

metrics associated with the age class just after MRA were no longer estimated using base WE93

equations. Instead they were assigned to the same values corresponding to one age class prior to

MRA, e.g., volume one year past MRA was set equal to volume one year prior to MRA although

no stand’s volume could decline below the 20-year base WE93 result. Conceptually the

trajectory of volume metrics retreats back down the curve after MRA is reached. This approach

is assumed to better reflect the reality of stand progression and forest succession.

The second enhancement relates to increases in productivity based on intensive management.

WE93 is a cross-sectional study of plot-level volume metrics. The assumption is that the

modeled response surface applies in a longitudinal context. However, WE93, when applied

longitudinally, reflects net change as opposed to any inherent growth potential. While realistic

under business as usual scenarios within some limits the original WE93 cannot reflect yield gains

associated with multiple entries, e.g., "thinning early, often, and heavy". Given this limitation,

WE93 model fitting information was used to develop a more realistic alternative for describing

the yields from intensive management. The original WE93 reported root mean square errors

(RMSE) for most equations, including BA and QMD. An RMSE is simply a measure of spread

of the error, the discrepancy between observed and predicted values. As WE93 plots used in

fitting were not filtered and include stands with various degrees of management through time,

the RMSE can be used as a proxy for more productive strands. Specifically, adding one RMSE to

the predicted value places that stand in a more productive subset relative to the mean predicted

value from the base WE93 equations. Adding one RMSE to both BA and QMD (and

subsequently propagating these enhanced predictions throughout the full system of WE93)

results in stands with higher volume, more BA, and larger but fewer trees. For this modeling

exercise the RMSE for the re-estimated equations were used, in absence of scaling factors, to

depict stands with enhanced productivity relative to their baseline counterparts. In general stands

were projected with these enhanced yield equations only as a result of a modeled management

action. However, white spruce and red pine plantations were always modeled with enhanced

yield equations.

WE93 utilized only 14 aggregated forest types. Consequently, forest type and forest type groups

present in the 2005 Minnesota FIA data were mapped to these aggregated types using the closest

and best match (Table 1). In order to calculate volume by species, as opposed to volume by type,

a species composition analysis based on net volume of growing stock on timberland acres from

the 2005 Minnesota FIA was done. The percentage of type volume in a given species was

calculated using the average percent composition across all plots in a given type and was

invariant across ownership, age, and site productivity.

In addition to merchantable volumes (cd ac-1

), QMD, and BA, logging residue (cd ac-1

) were also

5

tracked for each development type. Logging residue is the amount of expected residue remaining

after a typical final harvest operation adjusted for recoverability and management guidelines.

Residue figures used here were drawn from the 2006 Minnesota Logged Area Residue Analysis

Study (Sorensen, 2007) and are the sum of all residual types excluding standing volumes and

assume 50% recoverability (Table 3).

While not an explicit part of the yield tables, succession is an important component of growth

and yield. A successional matrix was developed using background information presented in the

GEIS. In this report, all plots that were harvested and visited both during the 1977 and 1990 FIA

periodic surveys had their cover type designations in 1977 and 1990 tabulated, effectively

tracking succession of one type to other types following harvest3.

In order to examine scenarios, the above land base, growth and yield tables, and

parameterizations for management entries were subsequently implemented in Remsoft

(Remsoft Inc., 2008), a forest estate and harvest scheduling model based on linear

programming.

Linear programming is an optimization technique where an algorithm searches for the "best"

solution—best being that solution that "satisfies" a mathematical objective function, e.g.,

maximization total cordwood volume harvested relative to a set of management constraints.

Since harvest level sustainability requires a longer-term time horizon, a 50-year planning horizon

consisting of ten five-year planning periods was used throughout. Thus all stands were projected

50 years into the future based on initial conditions in the 2005 FIA data. All management

activities (yield metrics) occur (were calculated) at the midpoint of any planning period.

Regulation over time in even-aged systems was encouraged with even flow constraints4. Such

ownership-specific even flow constraints reflect a lack of cooperation across land administrators.

Harvested volumes from the federal ownership class were constrained to not exceed 387,000 cd

yr-1

(see Thunderhawk). This figure is based on current annual targets on the Chippewa and

Superior National Forests and assumes that the volume per acre yield from the National Forests

applies to all lands in the federal ownership class. Finally, overall harvest levels were capped at

the 1996-2005 average at 3.69 mil cd yr-1

(Jacobson, 2007).

3 The tabulation used in this study was a weighted average between Tables 4.1 and 4.2 (assigned weights of 67%

and 33%, respectively) from Chapter 4 of the GEIS Forest Productivity Technical Report and was used in a

probabilistic fashion, e.g., a paper birch stand after final harvest will regenerate to red pine 4% of the time with

each regeneration outcome determined via a random number generator. 4 These function by restricting fluctuations in a given output over all planning periods. In the baseline scenario(s)

even flow constraints of 5% (the minimum value was constrained to be ≥ 95% of the maximum value) were

applied. These constraints were used for every combination of aggregated cover type (Table 1) and ownership

(Table 2). In addition, even flow of the statewide aggregated harvest volume, in total and for each ownership

singly, was also controlled at 5%.

6

Maximum Biological Growth Whereas the baseline scenario is designed to reasonably reproduce current management practices

and their outputs, e.g., treatment areas and volume harvested, maximum biological growth serves

only as a theoretical upper limit on harvest volume. Maximum growth was not determined using

Remsoft but rather under a set of simplified assumptions and FIA estimates of gross

productivity at culmination of mean annual increment (CMAI). The FIA compilation used in this

study (Miles and Pugh, 2007) contains a site productivity rating (SPCLASS) that provides a

range of gross volume growth (ft3 ac

-1 yr

-1) for each plot at CMAI assuming only a 1' stump.

CMAI was used to estimate maximum biological growth as follows: (1) For each developmental

type the shortest rotation age (Appendix 1) was used, uneven-aged systems had a 120-year

rotation and were treated as even-aged systems for this exercise. (2) These rotation ages were

then used to determine the areal extent of final harvest annually in an area control context

assuming a uniform age class distribution. For example, this exercise used 40 years for the aspen

type rotation age meaning that 2.5% of all aspen area was harvested annually with areas

harvested within SPCLASS proportional to their extent. (3) The growth of these stands, i.e., the

product of CMAI and area, was determined using the midpoint of each CMAI range (converted

using 79 ft3 =1 cd). (4) For the remaining age classes growth was estimated in the same manner;

effectively assuming that average growth across the entire age class distribution in a regulated

forest is approximated by CMAI. (5) Finally, CMAI is based on gross, not merchantable

volume. Simulations using WE93 showed that increasing dob from 0" to 3", i.e., moving from

gross to merchantable volume, reduced volume by 15%. This adjustment was used to scale raw

CMAI-based growth to the current standard of 3" dob.

Alternative Scenario Development Between the baseline scenario and the theoretical upper limit of potential harvest as constrained

by maximum biological growth there are an infinite number of plausible management outcomes.

As only a finite amount of these outcomes may be explored, 15 alternative scenarios were chosen

(Table 4). These scenarios were picked (1) to reflect management strategies that represent the

more feasible practices or serve as benchmarks similar to maximal biological growth and (2) to

quantify opportunity costs or gains relative to existing management practices. For example, total

harvest under the Cooperation scenario minus the same under the State Harvest scenario

quantified the gain in sustainable harvest level that would result from cross-ownership

cooperation. The difference between these two scenarios (Table 4) is that the ownership

cooperation parameter (Table 5) was changed from Nil (State Harvest) to Yes (Cooperation).

Each alternative scenario was developed by altering a single management constraint or parameter

(Table 4) relative to a benchmark scenario5. This approach allowed for the effects of multiple

changes in management policies to be aggregated. A key assumption was that the gains in

sustainable harvest volumes were additive. This assumption was tested with two additional

scenarios where five and ten management polices were altered simultaneously and contrasted

with altering the same polices singly. The underlying growth and yield equations, land base, and

time horizon were identical in all scenarios.

5 The Baseline (Federal Harvest) scenario was used to benchmark the State Harvest (Federal Unconstrained)

scenario. Otherwise the State Harvest scenario was used as a benchmark.

7

Results and Discussion Comparison of Baseline Scenario to Current Practices and FIA-based Growth Estimates The baseline scenario approximated recent forest management practice and utilization trends in

Minnesota. The targeted 3.69 mil cd yr-1

harvest level was achieved and the aspen harvest level,

which included bigtooth aspen, quaking aspen and balsam polar species, was within the range of

recently published estimates (Table 6). Harvest share, the percentage of volume harvested by

ownership class, was in good agreement with current practices; within 10% of harvest share for

private and county ownerships and within 1% for the state ownership. For federal lands the

discrepancy was proportionally larger (338,000 modeled vs. 239,000 actual cd yr-1

) and was

related to allowable sale quantities (ASQ). The modeled system assumes full achievement and

harvest of planned ASQs whereas recent practice reveals that only 50% of planned federal ASQs

were achieved (USDA Forest Service, 2007, 2008a). Furthermore, not all sold volume was

harvested, e.g., on the Superior National Forest 98%, 56%, and 48% of sold volume was

harvested in fiscal years 2005, 2006, and 2007 respectively (USDA Forest Service, 2007,

2008b,c). Remotely sensed estimates of statewide harvest activity including 2005 satellite

imagery ranged from 117,000 to 161,000 ac yr-1,

depending on the temporal and spatial coverage

of the base satellite imagery, and were accurate to ± 11,500 (Rack et al., 2007). These remote-

sensed estimates were likely underestimates of change as they excluded any harvest <5 ac and

failed to detect all partial harvest activity (Rack et al., 2007). The baseline scenario in

Thunderhawk (Johnson et al., 2007, Table C-36) showed harvest levels ranging from 133,000 to

171,000 ac yr-1

over the 40-year planning horizon used. The modeled 189,000 ac yr-1

areal

harvest extent was higher than both estimates but within 10%. Beyond harvest area and

cordwood, the baseline scenario produced 640,000 cd yr-1

in logging residue. Most roundwood

volume was generated in even-aged systems at final harvest, 87% by volume and 81% by area

treated (cf. Puettman and Ek, 1999). Furthermore, the aspen type played a pivotal role: 69% of

the statewide total harvested volume and 59% of all harvest activity by area was in the

aggregated aspen type.

In contrast to the baseline scenario, FIA-based estimates of growth exceeded current practices,

most prescriptive scenarios (Table 7), and the recommended 5.5 mil cd yr-1

target, i.e., maximum

biological growth (12.56 mil cd yr-1

)> gross growth (9.56 mil cd yr-1

)> net growth (5.00 mil cd

yr-1

)> current harvest levels (3.56–3.82 mil cd yr-1

). The baseline harvest level was 30% of

maximum biological growth, 40% of gross growth, and 75% of net growth. FIA-based estimates

of mortality are linked to a proximate cause of death. Mortality based on causes of death that

management activities can directly impact, i.e., fire, animal damage, insect, diseases, and

overstocking, totaled 1.04 mil cd yr-1

in 2005 (Miles, 2008). While it is not possible (or perhaps

even desirable) to capture (harvest prior to) all mortality, complete capture of net growth and this

portion of mortality would increase sustainable harvest levels by 2.35 mil cd yr-1

, and in excess

of the 5.5 mil cd yr-1

recommendation, to 6.04 mil cd yr-1

. Furthermore, FIA data provides some

guidance on relevant management actions to increase timber productivity. Each plot is assigned a

treatment opportunity code (Miles and Pugh, 2007), i.e., a recommended management activity to

restore a stand to full productivity. In the current Minnesota FIA 9.8 mil ac were assumed to

require either site preparation, stand conversion, thinning (partial, commercial, or

precommercial) or final harvest (Miles, 2008) with 4.3 mil ac (=44%) alone requiring final

harvest. These FIA-based figures are an order of magnitude greater than current and modeled

8

harvest rates, which range from 189,000 to 382,000 ac yr-1

depending on scenario. Even under

the scenario modeling the greatest acceleration in annual harvest acreages, 26 years would be

required to solely liquidate the current backlog of deferred management.

Changes in Timber Outputs by Scenario While comparisons between the baseline scenario and FIA-based estimates of growth offer some

insight into whether and how harvest levels could be increased toward the 5.5 mil cd yr-1

goal, a

more detailed understanding can be had using the remaining 15 prescriptive scenarios. These

alternative scenarios provided a means to quantify the cost, in terms of timber production, of

existing management practices and to project age class distributions and harvest by species, type,

and ownership through time. Social and environmental consequences of management practices,

while important, were not addressed.

In general, gains in harvested volume by scenario (Table 8) were roughly proportional to the

amount of area in each ownership group. This excluded the federal ownership, which was

constrained in most scenarios due to the difficultly in changing harvesting patterns on that

ownership. Apart from this, in most instances the amount of wood available for harvest increased

in all ownerships. Three exceptions to this occurred: the scenario reducing EERF on state lands

caused a less than 1% increase in harvest level in that ownership. This was enough to offset some

of the overall scenario-based increase on other ownerships because the suddenly available state

lands had, on average, higher productivity and yield than the acreages on county and private

lands they displaced. A similar dynamic occurred with allowing full access to private lands and

removing ASQs or EERF on federal lands. These scenarios shared that a single ownership being

targeted.

The increases in harvest level by scenario did not include logging residue (=0.64 mil cd yr-1

in

the Baseline scenario), which can also be used toward meeting the 5.5 mil cd yr-1

goal. This

averaged 0.73 mil cd yr-1

, was lowest in the Baseline scenario, and highest under No Even Flow

(=0.78 mil cd yr-1

). The areal proportion of harvest activity by treatment type and the percentage

of all volume in aspen species were relatively constant. The proportion of clearcut acres was

within 71–80% except for Accelerated Thinning (=60%). Here, the scenarios allowed access to

stands with lower productivity based on relaxed thinning regimes (Table 5). Aspen species

volume (Appendix 5), as a percentage of total volume, ranged from 43–48%. Variation in wood

quality, as measured by proxy using per acre yield (=19.5 cd ac-1

in the Baseline scenario), was

modest and ranged from 18.8 to 20.5 cd ac-1

. For 2 scenarios wood quality was outside of this

band: The highest wood quality was had under the Timber Stand Improvement scenario where

average yield was 22 cd ac-1

. The lowest was found under Accelerated Thinnings (=16.8 cd ac-1

),

which represented increased utilization of lower quality wood due to altered thinning regimes

(Table 5).

The scenarios (Table 4 & 5), considering only those that altered management policies singly,

with the largest effect (≈10% and greater) on harvest levels (Table 8) were No Even Flow (+0.81

mil cd yr-1

), Timber Stand Improvement (+0.52 mil cd yr-1

), State Harvest (+0.51 mil cd yr-1

),

Silviculture (+0.43 mil cd yr-1

), Federal Unconstrained (+0.28 mil cd yr-1

), and Accelerated

Thinning (+0.36 mil cd yr-1

). Federal Unconstrained and Silviculture were both speculative

scenarios. The former, coupled with the Federal Harvest scenario (a combined +0.38 mil cd yr-1

9

on federal lands alone, Table 8), quantified the opportunity costs of current federal management

practices relative to managing federal lands primarily for timber values. The Silviculture

scenario quantified the potential gain in harvest volumes assuming unlimited resources to solve

silviculture challenges in forest types such as lowland ash (cf. Jacobson, 2007). However, the

research investments needed are not currently in place.

Efficiency gains relative to market constraints (assuming full demand of available supply) and

process inefficacies in land management were evident when contrasting the Baseline with State

Harvest scenarios. The sole difference between the two was the removal of the statewide harvest

cap. The additional gain in harvested roundwood can be linked to efficiency in current practices,

i.e., the main tendencies of forestry in Minnesota produce exactly the 10-year average harvest

value only when capped at that level. Loss of efficiency in turning existing management

practices into harvested volumes occurred primarily when the amount of acres (and therefore

volume) slated for harvest based on ownership-specific planning procedures is not fully

harvested: treatment acres selected for management > timber sale administered acres > sold acres

> harvested acres > scaled acres. The State Harvest scenario assumes that this loss of harvestable

acres, and ultimately volume, was stopped; all scheduled acres were sold, harvested, and scaled

within one planning period (or ideally within one year of timber permit issuance). Additional

concerns related to market and process efficiency include: systematic overestimates of saleable

volume from growth and yield equations, the nonreplacement of acres after on-site appraisals

render these inoperable, market conditions relative to product availability, and the five-year of

growth that can occur over the life of a timber permit.

Both an increase in wood quality and volume were obtained under Timber Stand Improvement.

This scenario mandated that 10%, by area relative to the current harvest levels (=18,750 ac yr-1

),

be transitioned to enhanced productivity yield tables based on precommercial entries. In contrast

to this, Accelerated Thinnings reduced wood quality and provided a somewhat smaller increase

in harvest volumes. However, for both scenarios the use of biomass silviculture could defray

costs and, for the Accelerated Thinnings scenario, be used to channel low quality wood to

biomass markets.

The effects of even flow on harvest volumes were tested using two scenarios; even flow was

reduced from 5% to 10% and totally disabled. Without even flow constraints the modeled system

harvested twice the long-term sustainable level of 3.69 mil cd yr-1

in the first planning period.

This was followed by eight periods exactly at the 10-year average with, in the last planning

period, 27% of all volume harvested over the full planning horizon (=13.3 mil cd yr-1

) removed

in the last planning period alone. The initial burst in harvest activity is an artifact of removing

even flow. These constraints are the primary means the modeled system insures that nonmodeled

planning horizons are not compromised relative to the 50-year sustainable harvest level. Without

even flow constraints the age class distributions become highly skewed (see below) and further

removed from regulation (end of horizon effect).

However, the No Even Flow scenario was useful as a point in mapping the effects of

management policy by scenario to ending age class distributions—unlike harvest levels these are

not additive. Such distributions are especially useful for examining the implications for wildlife

habitat. Importantly, movement toward age class distributions with a similar number of acres in

10

each class suggests increasing stability in overall habitat over time. However, most age class

distributions were clumped around the baseline scenario result, as a change in a single

management policy did not, generally speaking, skew age class distribution. For discussion

purposes five model runs will be used, including the baseline (see Figures 1–8). In all cases only

even-aged types will be considered:

Aspen: Initially the aspen age class distribution was bimodal with a secondary

peak between minimum rotation age and MRA. In all scenarios this age class

imbalance of overmature wood was eliminated (Fig. 1); remnants existed

primarily to fulfill EERF targets on federal, and to a lesser extent, state lands. In

the No Even Flow scenario 3 mil ac (= 52%) alone was in the youngest age class

cohort after the full planning horizon; this skewness was visible in other age class

distributions as well but was most pronounced for aspen.

Balsam fir: As with aspen, the initial age class distribution was bimodal with an

initial peak around stand age 20 years and the primary peak centered around

minimum rotation age (Fig. 2). All scenarios, except No Even Flow, pushed the

age class distribution more toward regulation. Stands with older forest

characteristics were, as with aspen, maintained primarily on public lands.

Black spruce: Unlike balsam fir or aspen the area of younger cohorts, i.e., harvest

area, was clearly linked to scenario and an approach to regulation (an equal

number of acres in each age class) was difficult to discern. This is tied to

minimum rotation age (=90 yr), which was greater than the planning horizon (=50

yr). The initial unimodal distribution, centered at stand age 65 years, changed to a

bimodal distribution centered on younger cohorts (Fig. 3) and with a larger peak

at stand age 100 years.

Jack pine: Similar to black spruce, harvest activity varied with scenario (Fig. 4).

However, jack pine was the only shorter-lived type that did not see a significant

trend toward regulation. In the baseline scenario the ending area with ages >MRA

was greater than area with ages <MRA. This was linked to the large initial

imbalance in initial age class distribution, i.e., stand ages were skewed toward

inoperable stands, and relative lack of management activity in this type.

Paper birch: The initial mode of the distribution was centered at 65 years, slightly

beyond minimum rotation age. In all scenarios, except No Even Flow, a very clear

trend toward regulation was evident (Fig. 5) with the largest amount of older

stands, i.e., EERF on public lands, occurring under the baseline.

Red pine: Due to this type’s lifespan and minimum rotation age no trend toward

age class regulation was evident. The amount of harvest activity did vary by a

factor of two across all scenarios (Fig. 6) and the initial mode at age 35 years was

removed.

11

Tamarack: No clear approach to a regulated age class distribution was evident in

older aged stands. Regulation was only visible in the youngest cohorts. Otherwise,

the initial multimodal age class distributions were maintained (Fig. 7).

White Spruce: The area of the youngest cohort (harvest activity) varied with

scenario and ranged from 5–10 103 ac. All ending age class distributions had a

clear mode at age 65 years with a secondary peak at 85 years. This excludes the

No Even Flow scenario, which had substantially less area in older age cohorts

(Fig. 8). Only in the Baseline scenario was any forest area with stand age >115

left on the landscape.

Across all types, two main trends were present: (1) The No Even Flow scenario skewed age class

distributions such that regulation, i.e., a uniform age class distribution with, as applicable, some

held acres beyond normal rotation to satisfy nontimber values, was not achieved, and (2) longer-

lived types did not, and due to minimum rotation ages longer than the planning horizon could

not, show any approach to regulation.

Caveats While this analysis has quantified the effect of numerous management practices and

demonstrated that the 5.5 mil cd yr-1

key recommendation is within reach, caveats must be

mentioned: (1) The concept of market constraints (or market inefficiencies) can be recast as

limited demand, especially given the recent economic downturn, the 2006 harvest level of 3.1

mil cd yr-1

and anticipated lower harvest levels for 2007–2009. This highlights the desirability of

coupling wood supply with economic drivers. (2) The forest land base was undersampled and not

fully quantified in terms of map format information. Only 6,331 plots across 14.7 mil ac were

available. This limited any spatial interpretation and could mask feasibility issues within type.

(3) The private ownership group was highly aggregated such that any distinction between

industrial and nonindustrial ownership was removed. (4) The modeling of logging residue

assumes that this resource is unused. However, biomass consumers exist and their number is

increasing. This indicates that an unknown portion of this resource is already being utilized. (5)

The analysis focused primarily on the supply side, i.e., economic considerations, apart from the

involvement of the forest products industry in formulating the 5.5 mil cd yr-1

goal, were not

considered. Additionally, social and environmental impacts were not explicitly considered,

except to the extent that current and examined scenario management polices and regimes

incorporate them. It is further at the discretion of each stakeholder to determine the most

appropriate course of action based on overall wood supply, measures shown to increase timber

productivity, and social and environmental impacts. Finally, modeling was done at a strategic

level, across the entire state without considering regional differences. This is appropriate for

exploring statewide sustainable harvest levels but this approach has limits in terms of choosing

various strategies to increase harvest level on a regional or site-specific level. Despite these

caveats, and the approximation to reality inherent in any modeling exercise, the modeled system

clearly demonstrated growth potential in sustainable harvest levels in Minnesota, both relative to

the 5.5 mil cd yr-1

goal and in general.

12

Need for Additional Analysis Advisory Group recommendations for the future include maintaining and expanding capacity to

do a range of statewide analyses in a timely manner. Additional analyses should be expanded and

integrated to address:

A wide range of important ecosystem services (e.g. water quality, wildlife habitat, carbon

sequestration)

Information and assumptions related to private lands timber availability

Economics, including high valued-added products, forest management investment

opportunities, transportation needs, and impacts of potential budget limitations

Opportunities for integrating/mitigating increased biofuels production with timber

production and ecosystem services

Potential utilization and marketing opportunities and likely impacts

References

Birch, Thomas W. 1996. Private forest-land owners of the Northern United States, 1994

Resour. Bull. NE-136. Radnor, PA: USDA, Forest Service, Northeastern Forest

Experiment Station. 293 pp.

Jaakko Pöyry Consulting, Inc. 1994. Final Generic Environmental Impact Statement Study on

Timber Harvesting and Forest Management in Minnesota. Available:

http://iic.gis.umn.edu/download/geis/documnts.html (Accessed: 2008, May 1).

Jacobson, K. 2007. Minnesota’s Forest Resources. Division of Forestry, Minnesota Department

of Natural Resources, Saint Paul, MN, 66 pp. Available:

http://files.dnr.state.mn.us/forestry/um/minnesotaforestresources_rt2007.pdf (Accessed:

2008, May 1).

Johnson, B., et al. 2006. UPM/Blandin Paper Thunderhawk Project Grand Rapids, Minnesota

Final Environmental Impact Statement. Minnesota Department of Natural Resources,

Saint Paul, MN, 135 pp. plus appendices. Available:

http://files.dnr.state.mn.us/input/environmentalreview/UPMBlandin/feis.pdf (Accessed:

2008, May 1).

Miles, P.D., and S. Pugh. 2007. The RPA Summary Database Format Users Manual. Geographic

Information System Shapefile Version. Updated January 24, 2007. USDA Forest Service,

57 pp. Available:

http://www.ncrs2.fs.fed.us/4801/FIADB/fiadb_documentation/RPA_PLOT_SUMMARY

_DB_2007.pdf (Accessed: 2008, May 1).

Miles, P.D. 2008. Forest Inventory Data On-line 2.0 ver 1.0.0r4. USDA Forest Service, North

Central Research Station, St. Paul, MN. Available:

http://199.128.173.26/fido/mastf/index.html (Accessed: 2008, May 1).

Minnesota Department of Natural Resources. 2007. Report to the Governor: Governor’s Task

Force on the Competitiveness of Minnesota’s Primary Forest Products Industry.

Available: http://files.dnr.state.mn.us/publications/forestry/gov_taskforce/finalReport

07June15_draft.pdf (Accessed: 2008, May 1).

Minnesota Department of Natural Resources (n.d.) Subsection forest resource management

planning. Available: http://www.dnr.state.mn.us/forestry/subsection/index.html

(Accessed: 2008, May 1).

http://iic.gis.umn.edu/download/geis/documnts.html

http://files.dnr.state.mn.us/forestry/um/minnesotaforestresources_rt2007.pdf

http://files.dnr.state.mn.us/input/environmentalreview/UPMBlandin/feis.pdf

http://www.ncrs2.fs.fed.us/4801/FIADB/fiadb_documentation/RPA_PLOT_SUMMARY_DB_2007.pdf

http://www.ncrs2.fs.fed.us/4801/FIADB/fiadb_documentation/RPA_PLOT_SUMMARY_DB_2007.pdf

http://199.128.173.26/fido/mastf/index.html

http://files.dnr.state.mn.us/publications/forestry/gov_taskforce/finalReport

http://files.dnr.state.mn.us/publications/forestry/gov_taskforce/finalReport

http://www.dnr.state.mn.us/forestry/subsection/index.html

13

Puettmann, K.J., and A.R. Ek. 1999. Status and trends of silvicultural practices in Minnesota.

Northern Journal of Applied Forestry 16(4):203-210.

Rack, J., G. Deegan, and D. Kepler. 2007. Satellite-based Forest Change Monitoring of

Minnesota, 2004-2006. Resource Assessment, Division of Forestry, Minnesota

Department of Natural Resources, Grand Rapids, 7pp.

Remsoft Inc. 2008. Remsoft Spatial Planning System. 2008.8 Fredericton, New

Brunswick, Canada.

Schwalm, C.R. 2006. Technical Report–One Million Cord Analysis. Division of Forestry,

Minnesota Department of Natural Resources, Saint Paul, MN.

Sorensen, L. 2007. Minnesota Logged Area Residue. Division of Forestry, Minnesota

Department of Natural Resources, Saint Paul, MN, 24 pp. Available:

http://files.dnr.state.mn.us/forestry/um/mnloggedarea_residueanalysis.pdf (Accessed:

2008, May 1).

USDA Forest Service. 2004a. Land and Resource Management Plan Chippewa National Forest.

USDA Forest Service, Milwaukee WI. Available:

http://www.fs.fed.us/r9/forests/chippewa/projects/forest_plan/documents/cnf/index.shtml


USDA Forest Service. 2004b. Land and Resource Management Plan Superior National Forest.

USDA Forest Service, Milwaukee WI. Available:

http://www.fs.fed.us/r9/forests/superior/projects/forest_plan/2004Plan/snf/index.shtml


USDA Forest Service. 2007. FY 2005 Monitoring and Evaluation Report Superior National

Forest. USDA Forest Service, Milwaukee WI. Available:

http://www.fs.fed.us/r9/forests/superior/projects/2005monitoring.php (Accessed: 2008,

May 1).

USDA Forest Service. 2008a. FY 2007 Monitoring and Evaluation Report Chippewa National


http://www.fs.fed.us/r9/forests/chippewa/publications/monitoring_reports/ME2007_final.

pdf (Accessed: 2008, May 1).

USDA Forest Service. 2008b. FY 2006 Monitoring and Evaluation Report Superior National


http://www.fs.fed.us/r9/forests/superior/projects/2006Monitoring.php (Accessed: 2008,

May 1).

USDA Forest Service. 2008c. FY 2007 Monitoring and Evaluation Report Superior National


http://www.fs.fed.us/r9/forests/superior/projects/2007MonitoringReport.php (Accessed:

2008, May 1).

Walters, D.K., and A.R. Ek. 1993. Whole stand yield and density equations for fourteen forest

types in Minnesota. Northern Journal of Applied Forestry 10(2):75-85.

http://files.dnr.state.mn.us/forestry/um/mnloggedarea_residueanalysis.pdf



http://www.fs.fed.us/r9/forests/superior/projects/forest_plan/2004Plan/snf/index.shtml

http://www.fs.fed.us/r9/forests/superior/projects/2005monitoring.php

http://www.fs.fed.us/r9/forests/chippewa/publications/monitoring_reports/ME2007_final.pdf



http://www.fs.fed.us/r9/forests/superior/projects/2006Monitoring.php

http://www.fs.fed.us/r9/forests/superior/projects/2007MonitoringReport.php

14

Tables

Table 1. Type mappings and areal extent of timberland based on 2005 FIA data.

WE93 aggregated type

2005 FIA forest type

(LOCALTYPE)1

2005 FIA forest type group

(FORTYPE)1

Area (ac)

Jack pine Jack pine (101) White-red-jack pine (100) 362,019

Red pine Red pine2 (102) 395,397

White pine White pine (103) 77,695

Balsam fir Balsam fir (111) 392,603

Black spruce Black spruce (112) 1,335,027

Northern white cedar Northern white cedar (114) 571,913

Tamarack3 Tamarack (115), eastern red cedar

(135), other forest types (198)

885,024

White spruce White spruce2 (116) 111,063

Oak-hickory Oak-pine (140),

oak-hickory (150)

1,545,984

Elm-ash-soft maple Elm-ash-cottonwood (170) 1,222,258

Maple-birch Maple-birch (180) 1,569,212

Aspen Aspen-birch (190), aspen (191) 4,841,148

Paper birch Paper birch (192) 969,920

Balsam poplar Balsam poplar (194) 464,006

1 FIA field codes from Miles and Pugh (2007).

2 Red pine and white spruce stands with any evidence of artificial regeneration were mapped to the same

aggregated types but used enhanced productivity WE93 volumes (see text). 3 Types that could not be logically mapped to any WE93 types were placed here and amounted to <1.5% of the

total area modeled.

15

Table 2. Ownership classes and areal extent of timberland based on 2005 FIA data. Ownership

class

2005 FIA ownership (OWNER)1

Area (ac)

Federal National forest (11), bureau of land management (12),

other federal agencies (14)

2,001,391

State State (15) 3,849,425

County County and municipal (16) 2,119,618

Private 99 (Unknown)2 6,772,838

1 FIA field codes and values from the RPA shapefile (Miles and Pugh, 2007) enclosed in parenthesis.

2 Corresponds to OWNGRP = 4, nonindustrial private land. All private land carries this same designation.

Note: Approximately 272,820 acres were erroneously classified as state rather than county land in 2005 FIA data.

Figures in this study related to county and state ownerships should be adjusted accordingly.

Table 3. Cover type mappings used for logging residue (Sorenson, 2006). WE93 aggregated type Logging residue type

1

Jack pine Upland conifers

Red pine Upland conifers

White pine Upland conifers

Balsam fir Upland conifers

Black spruce Lowland conifers

Northern white cedar Lowland conifers

Tamarack Lowland conifers

White spruce Upland conifers

Oak-hickory Other hardwoods

Elm-ash-soft maple Other hardwoods

Maple-birch Other hardwoods

Aspen Aspen

Paper birch Aspen

Balsam poplar Aspen

16

Table 4. Name and parameter grid for all 16 scenarios. A dashed entry indicates the absence of

that constraint or management practice (see Table 5 for parameter definitions).

Scenario name

Harvest

limits

EERF

Ow

ner

ship

coo

per

atio

n

Sil

vic

ult

ure

co

nst

rain

ts

Ev

en f

low

Acc

eler

ated

thin

nin

gs

Inte

nsi

ve

thin

nin

gs

Inte

nsi

ve

man

agem

ent

Ro

tati

on

ag

es

Pri

vat

e la

nd

s

avai

lab

ilit

y

Baseline Overall Both Nil Yes 5% - - - Baseline 90%

State harvest Federal Both Nil Yes 5% - - - Baseline 90%

Federal harvest - Both Nil Yes 5% - - - Baseline 90%

State ERF Federal Less State Nil Yes 5% - - - Baseline 90%

Federal

unconstrained

- Less

Federal

Nil Yes 5% - - - Baseline 90%

Cooperation Federal Both Yes Yes 5% - - - Baseline 90%

Silviculture Federal Both Nil - 5% - - - Baseline 90%

Relaxed even

flow

Federal Both Nil Yes 10% - - - Baseline 90%

No even flow Federal Both Nil Yes - - - - Baseline 90%

Accelerated

thinning

Federal Both Nil Yes 5% Yes - - Baseline 90%

Intensified

thinnings

Federal Both Nil Yes 5% - Yes - Baseline 90%

Intensified

management

low

Federal Both Nil Yes 5% - - 10% Baseline 90%

Intensified

management

high


Accelerated

harvest

Federal Both Nil Yes 5% - - - Accelerated 90%

Full private

lands

Federal Both Nil Yes 5% - - - Baseline 100%

Timber stand

improvement


17

Table 5. Scenario parameter key. Constraint Parameter

Harvest Limits Overall Harvest limits are 387,000 cd yr

-1 on

federal lands and 3.69 mil cd yr-

1statewide

Federal Harvest limits are capped at 387,000 cd yr

-

1on federal lands only

ERF Both Effective ERF

targets on state

and federal

lands (App. 1)

Less State State effective ERF targets

reduced from the Baseline to the

Governor’s Task Force statewide

planning figure of 9.4%. Note:

EERF for balsam fir was

unchanged; its Baseline value was

less than 9.4%. Federal EERF

follows App. 1

Less Federal Effective ERF targets on

state lands; federal EERF

no less than Governor’s

Task Force

recommendation of 9.4%

(App. 1)

Ownership

Cooperation

Yes

Even flow constraints on each

aggregated type only. This allows the

four ownership subgroups to work

toward regulation in concert.

Nil Even flow constraints for each

combination of ownership and aggregated

type

Silviculture

Constraints

Yes Areal operability limits implemented for northern white cedar, elm – ash, and maple –

birch types; tamarack species harvest limited to 70,000 cd yr-1

.

Even Flow Proxy for emphasis on age class regulation; determines the amount of between-period

fluctuation of harvested volume, e.g., 5% means that the minimum period value must be

95% of the maximum value.

Accelerated

Thinnings

Yes Reentry intervals of 10 yr in all thinnable types; stocking thresholds reduced by 50%

relative to Appendix 2. For aspen, the earliest thinnable age is lowered to 3 periods.

Intensive

Thinnings

Yes All thinned stands are transitioned to enhanced productivity yield tables.

Intensive

Management

A fixed percentage of clearcut stands

are transitioned to enhanced

productivity yield tables.1

For the Timber Stand Improvement

scenario 10% of baseline harvest acres

(= 18,750 ac yr-1

) linked to enhanced

productivity yield tables after a timber

stand improvement.1,2

Rotation Ages Baseline Rotation ages as given in Appendix 1

Accelerated All even-aged systems may be harvested

one planning period earlier. This is meant

to reflect an emphasis on economic vs.

biological rotation ages.

Private Lands Percentage of private lands, by area, operable1

1 The model chooses which stands are operable or linked to enhanced productivity yield tables based on

volume maximization. 2 A precommercial timber stand improvement generates no merchantable volume and can occur in all types

with stand age ≤ 40 yr.

18

Table 6. Volume harvested in the baseline scenario compared to published estimates.

Total volume harvested

(mil cd yr-1

)

Total aspen species1

volume harvested

(mil cd yr-1

)

Source

3.69 1.82 Baseline scenario, annualized from full 50-yr

planning horizon

3.72 2.01 Jacobson (2007), pg. 19 reporting year 2005

3.69 2.28 Jacobson (2007), pg. 19 average from reporting

years 1996 – 2005 for total volume and pg. 32

from reporting years 1994 – 2005 for aspen

3.23 1.67 Removal of all live on forestland from the 2005

Minnesota FIA2

3.32 1.77 Average removal all live on forestland from the

2004 – 2007 Minnesota FIA 1 Aspen species includes bigtooth aspen, quaking aspen and balsam polar.

2 Removals reference harvested and utilized trees only. Using all live on forestland eliminates problems

due to land changing from timberland to unproductive or reserved forestland through time (P. Miles,

Northern Research Station USDA Forest Service, pers. comm.). FIA population estimates are from Miles

(2008).

Table 7. FIA-derived estimates of growth, mortality, and maximum biological growth contrasted

with ranges of prescriptive scenarios and current harvest levels. All FIA-based estimates are

averages from the 2004 – 2007 Minnesota annual inventories and reference all live trees on

forest land. Growth or

Harvest Level

Volume

(mil cd yr-1

)

Source

Net growth 5.00 Miles (2008)

Mortality 4.56 Miles (2008)

Gross growth 9.56 Net growth + mortality

Maximum biological growth 12.56 This study

GEIS harvest levels 4.79 – 7.00 GEIS

Thunderhawk harvest levels 3.85 – 5.24 Thunderhawk

1996-2005 annual harvest levels 3.56 – 3.82 Jacobson (2007)

19

Table 8. Changes in sustained timber yield by ownership relative to current practices estimated

by altering selected management practices and policies singly. A dashed entry indicates no data.

Statewide totals may differ due to rounding.

Change in harvest by ownership

(1000s cd yr-1

)

Management practice County Federal Private State Overall

Addressing market and process constraints +95 +49 +184 +181 +509

Reducing ERF on state lands

Reducing EERF from current (12%) to Governor’s Task

Force (9.4%) value

-1

-

-1

+19

+17

Federal lands

Removing ASQ-based caps on harvest

Reducing extent of management areas that emphasize old

forest characteristics

+3

-11

+68

+311

+3

-24

0

0

+68

+276

Cross ownership cooperation +63 - +61 -3 +120

Full access to private lands -1 - +15 0 +17

Intensive management used to restore full productivity on a fixed

percentage of final harvest by area

10%

25%

+5

+9

-

-

+10

+28

+2

+4

+17

+41

Addressing operability constraints in sensitive types +95 - +66 +265 +426

Reducing rotation ages in even-aged types by 5 yr +29 - +87 +74 +191

Precommercial timber stand improvement used to restore full

productivity on 18,750 ac yr-1

+117 - +299 +104 +520

Less emphasis on age class regulation +3 - +22 +8 +33

No emphasis on age class regulation +155 - +411 +243 +808

Aggressive thinning

“Early, often, and hard”

Thinned stands intensively managed

+65

+32

-

-

+154

+59

+143

+24

+362

+116

Logging residue

Increased utilization of harvest residue in even-aged systems

+121

+59

+334

+127

+640



20

Figures

Figure 1. Age class distribution of the aspen cover type (including balsam poplar; total area = 5.3 mil ac). Dashed

line indicates initial (black) and ending (red) distributions from the Baseline scenario. Solid lines indicate ending age

class distribution from 4 alternative scenarios: Federal Unconstrained (green), Accelerated Harvest (blue), Timber

Stand Improvement (cyan), and No Even Flow (magenta). Solid vertical line indicates maximum rotation age. The

final age class contains all acres at or beyond the 145 yr midpoint. The youngest age class under No Even Flow has

3 mil ac at scenario end.

21

Figure 2. Age class distribution of the balsam fir cover type (total area = 0.4 mil ac). Dashed line indicates initial

(black) and ending (red) distributions from the Baseline scenario. Solid lines indicate ending age class distribution

from 4 alternative scenarios: Federal Unconstrained (green), Accelerated Harvest (blue), Timber Stand Improvement

(cyan), and No Even Flow (magenta). Solid vertical line indicates maximum rotation age. The final age class

contains all acres at or beyond the 145 yr midpoint.

22

Figure 3. Age class distribution of the black spruce cover type (total area = 1.3 mil ac). Dashed line indicates initial



(cyan), and No Even Flow (magenta). The final age class contains all acres at or beyond the 145 yr midpoint.

23

Figure 4. Age class distribution of the jack pine cover type (total area = 0.4 mil ac). Dashed line indicates initial





24

Figure 5. Age class distribution of the paper birch cover type (total area = 1 mil ac). Dashed line indicates initial





25

Figure 6. Age class distribution of the red pine cover type (total area = 0.4 mil ac). Dashed line indicates initial




26

Figure 7. Age class distribution of the tamarack cover type (total area = 0.9 mil ac). Dashed line indicates initial




27

Figure 8. Age class distribution of the white spruce cover type (total area = 0.1 mil ac). Dashed line indicates initial




28

Appendix A

Appendix Table A1. Rotation ages and effective ERF percentages for

even-aged types.


Rotation

age1,2

State ERF

(effective %)3

Maximum

rotation age4

Jack pine 11 (10) 9.6% 15

Red pine 22 (12) 12.1% 38

White pine 35 (12) 39

Balsam fir 10 (8) 7.0% 13

White spruce 14 (10) 16

Black spruce 20 (18) 12.3% 30

Tamarack 14 (18) 14.2% 36

Elm – ash – soft maple 24 (18) 24

Maple – birch 24 (18) 24

Aspen 9 (8) 11.8% 15

Paper birch 11 (10) 11% 16

Balsam poplar 9 (8) 11.8% 15 1 Rotation ages are in five-year age classes and correspond with the five-year planning

periods used in the model. For example, on state lands aspen rotation is 9 age classes,

which corresponds to a stand age of 41–45 yr. Stand age was the only operability

criterion used for even-aged stands except for the elm-ash-soft maple and maple-birch

types which also required a site index < 50. 2 Rotation ages are for the state ownership class with the federal value in parenthesis.

County and private rotation ages were always one planning period less than state

rotations ages, e.g., rotation age for black spruce on county and private lands was 19 age

classes. Red pine, an exception, had a rotation age of 12 age classes on county and

private lands. State rotation ages were taken from Schwalm (2006). Federal values were

taken from table S-TM-5 of the current Chippewa National Forest plan USDA Forest

Service (2004a). 3 Effective ERF was considered only for major forest types. The percentages refer to the

amount of the forest type held beyond normal rotation on state lands only. For federal

lands 29.1% of all listed types were targeted as effective ERF in the first 50 years past

normal rotation with an additional 14.6% targeted after this initial 50-yr period. This

corresponds to the percentage of operable acres in "Longer Rotation", "Recreation Use

in a Scenic Landscape", "Semi-primitive Non-motorized Recreation", and "Semi-

primitive Motorized Recreation" management areas as listed in the current management

plans for the Chippewa and Superior National Forests. These management areas

emphasize older rotations and/or old forest characteristics and were assumed

functionally equivalent to EERF on state lands. State EERF values are from Schwalm

(2006). Federal figures were estimated using current Forest Service planning documents

USDA Forest Service (2004a,b). 4 Maximum rotation ages (in five-year age classes) were used in yield table generation

only and were invariant across ownership type. The types not listed here used 24 age

classes as MRA.

29

Appendix Table A2. Operability criteria for uneven-aged types. All systems used a 20-yr

reentry interval, removed 33% of standing volume, and transitioned to a regulated diameter

distribution after the first management entry1.


Operability

criteria

Elm – ash – soft maple Site index > 50

BA > 120ft2

QMD > 7"

Maple – beech – birch Site index > 50

QMD > 7"

Oak – hickory QMD > 9"

Northern white cedar Age of oldest

cohort > 70 yr 1 For regulated stands age-invariant volumes from

Thunderhawk (H. Hoganson, Thunderhawk

contributor, pers. comm.) were used.

Appendix Table A3. Operability criteria for types where thinnings were allowed. Reentry

intervals were 1five-year for all types except for aspen, which could only be thinned once.

Thinnings removed 33% of standing volume.

WE93 aggregated type Operability criteria

Aspen Site index > 70

Stand age 30–40

Red pine Site index > 50

BA > 120ft2

Stand age < 160

White pine Site index > 50

BA > 120ft2

Stand age < 100

Jack pine Site index > 60

BA > 75ft2

Stand age < 50

White spruce Site index > 50

BA > 75ft2

Stand age < 60

30

Appendix Table A4. Harvested volume by ownership and areal extent of management by

treatment type for all scenarios.

Volume harvested by ownership

(1000s cd yr-1

)

Area by treatment type

(1000s ac yr-1

)

Scenario

County

Federal

Private

State

Total

Even-

aged

Thin

Uneven-

aged

Baseline 697 338 1,925 730 3,690 154 20 15

State Harvest 792 387 2,109 911 4,199 165 28 18

Federal Harvest 789 455 2,112 911 4,267 166 29 18

State ERF 791 387 2,108 930 4,216 166 29 19

Federal

Unconstrained

778 766 2,087 911 4,543 180 30 19

Cooperation 855 387 2,169 908 4,319 171 29 19

Silviculture 887 387 2,175 1,176 4,625 177 28 38

Relaxed Even

Flow

795 387 2,130 919 4,231 165 29 19

No Even Flow 947 387 2,518 1,154 5,006 187 32 44

Accelerated

Thinning

857 387 2,263 1,054 4,561 164 90 18

Intensified

Thinnings

825 387 2,168 935 4,315 164 28 18

Intensified

Management

Low

797 387 2,119 913 4,216 166 28 18

Intensified

Management

High

801 387 2,137 915 4,240 165 30 19

Accelerated

Harvest

821 387 2,196 985 4,389 182 29 19

Full Private

Lands

791 387 2,124 911 4,213 166 28 19

Timber Stand

Improvement

909 387 2,408 1,015 4,719 166 30 19

Note 1: Current full Allowable Sale Quantity (ASQ) on federal national forest lands = 387

Note 2: Approximately 272,820 acres were erroneously classified as state rather than county land in 2005 FIA data.


31

Appendix Table A5. Harvested volume by species for all scenarios. Volume harvested by species

1

(1000s cd yr-1

)

Scenario

Aspen

Paper

Birch

Red

Pine

White

Pine

Jack

Pine

Black

Spruce

White

Spruce

Tamarack

Balsam

Fir

Northern

White Cedar

Basswood

Maple

Ash

Oak

Other

Total

Baseline 1,822 335 223 49 105 134 83 70 218 53 84 116 103 163 131 3,690

State Harvest 2,001 425 250 56 144 157 98 70 253 66 95 131 117 184 150 4,199

Federal Harvest 2,008 436 264 68 154 158 101 70 257 67 95 132 117 185 151 4,267

State ERF 2,007 426 250 57 144 164 98 70 254 66 95 132 117 184 151 4,216

Federal

Unconstrained

2,082 454 316 79 176 210 110 70 270 70 98 138 124 191 157 4,543

Cooperation 2,017 435 292 60 149 161 107 70 262 68 99 134 120 188 157 4,319

Silviculture 2,011 424 251 59 144 187 99 260 263 187 103 137 139 189 169 4,625

Relaxed Even

Flow

2,009 426 258 57 145 161 99 70 254 66 97 132 118 186 153 4,231

No Even Flow 2,193 450 384 70 158 238 119 70 279 71 152 160 158 271 233 5,006

Accelerated

Thinning

2,090 427 412 80 174 161 120 70 265 65 97 136 119 190 154 4,561

Intensified

Thinnings

2,077 433 252 58 146 159 100 70 260 67 97 135 120 189 153 4,315

Intensified

Management

Low

2,011 426 251 57 145 158 99 70 254 66 95 132 117 185 151 4,216

Intensified

Management

High

2,025 428 251 57 146 158 99 70 256 66 96 133 118 186 151 4,240

Accelerated

Harvest

2,086 418 289 60 150 187 103 70 261 65 98 136 123 189 156 4,389

Full Private

Lands

2,005 426 252 57 144 158 99 70 254 66 96 132 118 186 152 4,213

Timber Stand

Improvement

2,312 465 259 63 169 165 108 70 290 72 102 148 129 205 162 4,719

1 Aspen species includes bigtooth aspen, quaking aspen and balsam polar. Minor hardwood species, i.e., all hardwoods excluding paper birch and aspen were

aggregated, e.g., maple includes all maple species (hard and soft). Other includes volume from hardwoods and softwoods as well as trees with unknown or

missing species codes.

32

Appendix B: Additional Scenario

Original June 10, 2009

Last Revision June 19, 2009

The main scenarios (Tables 4-5 & 8 in the body of the report) quantify the change in harvest

level relative to changing one management policy. Beyond this, one additional scenario was

added at the request of several Advisory Group members to address a desire for analysis of a

single harvest or timber yield level, overall and by species that would be achievable given

several altered environmental and social constraints. To estimate these harvest levels an

additional scenario was designed that removed ―market‖ constraints, eased even-flow constraints

on several forest types, and altered three policies or practices at once. This is not a

―recommended‖ harvest level, but is instead an examination of timber yields and age-class

impacts given the scenario parameters and constraints

This scenario is based on adjustments to the Baseline scenario and includes all of the flowing

changes: (1) the upper limit on total statewide harvest volumes was disabled, (2) the upper limit

on tamarack species harvest levels was disabled, (3) even flow constraints on the black spruce,

elm-ash-soft maple, maple-birch, and tamarack types were disabled, (4) areal operability limits

on elm-ash-soft maple and maple-birch stands of poor site quality were disabled, (5) timber stand

improvement treatments (see Table 4) were performed on 18,750 acres annually, (6) accelerated

thinning regimes were used (see Table 5), and (7), all thinning results in stands restored to full

productivity, i.e., stands were, after treatment, indexed to the enhanced productivity yield tables.

Overall the total statewide aggregate harvest level was, annualized over the full planning

horizon, 6.02 mil cd yr-1

. This figure is for roundwood only, i.e., excludes logging residue, and

exceeds the 5.5 mil cd yr-1

recommendation of the 2007 Governor’s Task Force on the

Competitiveness of Minnesota’s Primary Forest Products Industry. The aspen species group had

the largest single share (= 41%) of overall volume (Table B1). Average yield was 18.5 cd ac-1

and the areal share of even-aged treatments was 58%. These three figures were lower than in the

Baseline and reflected an increased emphasis on thinnings and greater access to stands with

lower wood quality. In general, age class distributions of shorter-lived even-aged types all

achieved a higher degree of regularization after the 50-yr planning period (Figure B1).

33

Table B1. Harvested roundwood volume by species, ownership, and overall.

Species

Volume harvested1

(1000s cd yr-1

) Aspen 2,477

Paper Birch 490

Red Pine 431

White Pine 95

Jack Pine 223

Black Spruce 279

White Spruce 135

Tamarack 274

Balsam Fir 319

Northern White Cedar 91

Basswood 179

Maple 190

Ash 211

Oak 336

Other 289

Ownership

Volume harvested (1000s cd yr

-1)

County 1,141

Federal 387

Private 3,081

State 1,410

Total 6,020 1

Aspen species includes bigtooth aspen, quaking

aspen and balsam polar. Minor hardwood species,

i.e., all hardwoods excluding paper birch and aspen

were aggregated, e.g., maple includes all maple

species (hard and soft). Other includes volume

from hardwoods and softwoods as well as trees

with unknown or missing species codes.



34

Figure B1. Area (1000’s of acres) over 10-yr age class (class midpoints) with starting (red) and ending (black) age

class distributions for all even-aged cover types. The age class distribution goal (horizontal gray) assumes full

regularization at the average of federal and state normal rotation ages. Vertical lines indicate normal (solid gray) and

maximum (dashed gray) rotation ages. Maximum rotation ages for black spruce, tamarack and red pine all exceed

145 years.

35

Appendix C: Summary Document Appendix C is a stand-alone 5 page summary document to be used as a communication tool

for the analysis. It was prepared at the request of the Advisory Group.

---------------------------------------------------------------------------------

Forest Harvest Levels in Minnesota Effects of Selected Forest Management Practices on Sustained Timber Yields

Purpose

The Statewide ―Harvest Level‖ Analysis was done in order to:

1) Provide information to policymakers, forest managers and proposers of new industrial facilities

to help assess future timber yields and forest age classes under a range of potential management

and policy options that might help increase statewide harvest levels.

2) Meet a key recommendation of the Governor’s Task Force on the Competitiveness of

Minnesota’s Primary Forest Products Industry, to ―explore the feasibility of sustainably

increasing total statewide timber production for all ownerships to 5.5 million cords per year.‖

3) Enable additional future analyses of key impacts to Minnesota’s forest resources from changes

in forest condition, and proposed policies.

Key findings

1) A 5.5 million cord harvest level is attainable. Two items necessary to achieve this level would

be improved markets for a wide range of species and products, and increased investments in

forest management practices.

2) Specific opportunities for raising harvest levels through intensified management include

intensified ―commercial‖ and ―precommercial‖ thinning and stand treatments in several forest

types such as red pine, addressing market and process constraints, and addressing regeneration

challenges in forest types such as white cedar.

3) See pages 3 and 4 for a summary of harvest level impacts of the scenarios.

Key Details

Examined estimated harvest level impacts across ownerships of changes to selected forest

management practices and policies including: additional thinning, reducing rotation ages,

―precommercial‖ stand treatments, and others.

The analysis used state-of-the-art forest modeling techniques, is subject to key constraints and

limitations, and uses baseline data from the USDA Forest Service Forest Inventory and Analysis

(FIA) 2003 all-ownership forest inventory.

A multi-stakeholder Advisory Group provided input and guidance.

The Advisory Group made recommendations for future research, analysis and implementation

work that was beyond the scope of this initial analysis.

Quantification of potential harvest volume is one important factor that can be considered by

decision makers, in addition to environmental and social benefits. Decisions on forest

management policies and/or and investment levels are outside the scope of this analysis.

The focus is largely on resource analysis, i.e., potential yields rather than social, environmental

or economic, and as such the study is more limited in scope than the 1994 Generic

Environmental Impact Statement on Timber Harvesting (GEIS). However, the study did


consideration of old growth, etc. Thus aspects of various social, economic and environmental


The task of assessing effects of forest management is never complete but is merely updated.

36

This analysis should be viewed in this same vein.

What the analysis is not.

A re-do of the Generic Environmental Impact Statement (GEIS) on timber harvesting.

Resources were not available for a comprehensive update of the GEIS.

An effort to dictate how different landowners manage their forests. Harvest level impacts are

only one important consideration when examining forest management and policy options and

the analysis is not designed to explicitly examine the social or environmental desirability of

these options.

The final word on sustainable harvest levels. Sustainable harvest level examination is a

continuing process as the forest changes, and as additional information needs and/or analysis

parameters or methods are identified.

Recommendations for Future Analysis The Advisory Group made recommendations that go beyond the scope of this initial analysis. They

recommend that it is important to maintain and expand capacity to do a range of statewide analyses in a

timely manner and that harvest-level analyses should be updated regularly. Additional analyses should

be expanded and integrated to address:

A wide range of vital ecosystem services (e.g. water quality, wildlife habitat, carbon

sequestration, etc.)

Information and assumptions related to private lands timber availability

Economics, including high valued-added products, forest management investment opportunities,

transportation needs, and impacts of potential budget limitations

for integrating/mitigating increased biofuels production with timber production and ―ecosystem

services‖

Potential utilization and marketing opportunities and likely impacts

Harvest Level Advisory Group An Advisory Group was formed to provide assistance in developing the analysis through peer review

and input. The Advisory Group reflects a breadth of diversity in management and administration

statewide, and includes representatives from state and federal government, counties, and the scientific

community.

Members: Minnesota DNR

Dave Epperly - Director, Forestry Division

Dave Schad - Director, Fish & Wildlife Division

Steve Hirsch - Director, Ecological Resources Division

Christopher Schwalm - Forest Research Scientist

Keith Jacobson - Program Supervisor

University of Minnesota College of Food, Agricultural and Natural Resources Sciences

Alan Ek - Professor and Department Head, Department of Forest Resources

Howard Hoganson – Professor, Department of Forest Resources

United States Department of Agriculture - Forest Service

Rob Harper - Supervisor, Chippewa National Forest

Mike Prouty - Field Representative, St. Paul Field Office, State & Private Forestry

Tom Schmidt - Assistant Director, Northern Research Station

37

Minnesota Association of County Land Commissioners

Robert Krepps, Past Chair

University of Minnesota – Duluth, Natural Resources Research Institute

Mike Lalich – Director

Minnesota Forest Industries

Tim O’Hara - Vice President for Forest Policy

For more information: Christopher Schwalm, Chief Author, MN DNR Research Scientist:

[email protected] or

Keith Jacobson, MN DNR Forest Products Utilization Program Supervisor, Email:

[email protected]

mailto:[email protected]

mailto:[email protected]

38

Changes in base harvest level by ownership Estimated by altering management practices and policies.

Notes:

1. The table below contains a list of the various forest management practices and policy scenarios examined for their estimated harvest level impacts.

2. Base harvest level = 2005 Minnesota timberlands forest harvest volume, or 3.7 million cords.

3. A dashed entry indicates no data.

4. Statewide totals may differ due to rounding.

5. Harvest level impacts are one important consideration in assessing management practices and policies. Social and environmental impacts also should be considered.

6. An FIA inventory data error in assigning state/county ownership was discovered late in the process of preparing this analysis and report. Approximately 272,820 acres

classified as county land in 2003 data were erroneously classified as state land in 2005 data (6/23/2009 personal communication, Pat Miles, FIA Analyst). Figures in this

study related to county and state ownerships should be adjusted accordingly.

Management practice with brief description


(1000s cd yr-1

)

Implementation notes County Federal Private State Total

Intensified thinning Two scenarios as described in

next column

Stands entered sooner with shorter re-entry

intervals and smaller residual basal areas.

Focus solely on merchantable stock; control

for insects, disease, increased competition

control; stands fertilized where needed.

+97 -- +213 +167 +478 Likely to require significant additional investment in staff

time to accomplish. Good potential harvest level benefits.

Possible habitat benefits in some forest types such as red

pine. Low political risk, in cover types such as red pine with

the greatest volume impacts.

Precommercial timber stand improvement

Precommercial and cultural treatments used to restore full productivity

on 18,750 ac yr-1

. Such treatments include any action before

merchantability (stand age < 40 yr) that restores full productivity.

Seedbed treatment, conversion, fertilization, etc. are examples. Note also

that this scenario applies only 10% , by area, of long-term (pre-downturn)

harvest acreages.

+117 -- +299 +104 +520 Would require significant capital investment to achieve, but

some real potential.

Controversy likely to be modest for some practices such as

conversion to appropriate ecological type, and higher for

others such as fertilization.

Future analyses should focus on developing a better

understanding of direct gains from investments as compared

to indirect impact of assumed future investments on current

allowable cuts.

Addressing market and process constraints Example: Insuring that all planned management activities are fully

operationalized, from initial administration through to scaling, within a 5-

yr timeframe (ideally quicker).

+95 +49 +184 +181 +509 Would require market development, silviculture investments

and perhaps shorter permit time lengths.

Likely to require significant additional investment in staff

time to accomplish. Also, readers should be aware that a

certain percentage of sites will not result in a management

activity, due to market conditions and/or inventory quality.

This should go down over time as inventory improves, but

will never be zero.

39



(1000s cd yr-1

)


Cross ownership cooperation

Allowing all 4 ownerships to effectively trade-off areas to satisfy

common statewide even flow goals.

+63 -- +61 -3 +120 Promote management goals that transcend ownership

boundaries, e.g., ERF and/or spatial planning issues at a

landscape level irrespective of ownership.

Worthy of further study. Different management goals across

ownerships make achieving this difficult.

Addressing regeneration constrains in difficult types

Lowland black ash and white cedar are the two forest types with

opportunities.

+95 -- +66 +265 +426 Would require significant capital investment in silviculture

research to achieve. Finding money for a position or 2 to

focus on this would be a good start.

Controversy likely to be low, with potential for fairly wide

stakeholder support.

Intensive management used to restore full productivity on a

fixed percentage of final harvest by area

Full productivity is based on full stocking with cover type

matching Native Plant Community. Management actions used at

final harvest to restore productivity include stand conversion,

fertilization, and stocking control.

Two scenarios, with different percentages of even-aged final harvest by area

restored to full productivity.

10%

25%

+5

+9

--

--

+10

+28

+2

+4

+17

+41

Would require significant capital investment to achieve.

Controversy likely to be low for some of these activities,

with potential for fairly wide stakeholder support.

Reducing minimum rotation ages in even-aged types by 5 years +29 -- +87 +74 +191 Could implement this over time on at least a portion of lands

as new management plans are completed.

Potential for political sensitivity.

Age class regulation

In general, the less age class regulation, the more

volume, and the further away age class distribution

moves from regulation. Volume gains are typically

short term as age class regulation is less rewarded

by the model internally and a boom-bust cycle is

created.

Two scenarios:

Less emphasis on

age class regulation

No emphasis on age

class regulation

+3

+155

--

--

+22

+411

+8

+243

+33

+808

Potentially negative age class consequences, but there would

be some opportunities to relax even flow constraints to

address some age-class imbalances and still achieve desirable

age class structure.

Potential for very negative age class consequences without

some constraints on harvest flows or forest conditions.

Potentially valuable for future analyses to explore other

scenarios and even-flow policies.

Reducing Extended Rotation Forests (ERF) on state lands

From current levels to 30% prescribed (12% to 9.4% actual) as

recommended by Governor’s Task Force on Primary Forest Industry.

-1 -- -1 +19 +17 Potential for negative habitat diversity and biological

diversity impacts.

Likely to be contentious with stakeholders.

Future analyses may be useful in identifying opportunities

for better guiding ERF implementation.

40



(1000s cd yr-1

)


Federal lands Two scenarios as described in next column

Removing Allowable Sale Quantity

(ASQ)-based caps on harvest

Reducing extent of management

areas that emphasize old forest

characteristics

-3

-11

+68

+311

-3

-24

0

0

+68

+276

According to advisory group member Rob Harper, federal

laws and regulations require vegetation management on the

National Forests be balanced with other forest uses, and that

management decisions be transparent to the public.

Increased timber sale quantities would require public

engagement, and possibly significant analysis, therefore

increases may not be realized in the short term.

Utilization of logging residue

Increased utilization of harvest residue in even-aged systems. Guideline

impacts are reflected in these figures.

+121 +59 +334 +127 +640 Good opportunity to provide additional volumes to some

markets such as energy.

Readers should note that a portion (less than 20%) of this

volume is currently being utilized.

Full access to private lands

All private lands are operable as opposed to 90% under the baseline

scenario.

Private lands availability is a critically important issue. To understand

why the volume gain in this analysis of adding the final 10% of private

lands are so modest, it is important to realize that the model tends to

choose stand with the most volume first.

-1 -- +15 0 +17 More analysis is needed on the issue of private lands

availability and management—a critical issue for Minnesota.

Additional scenario

The main scenarios quantify the change in harvest level relative to

changing one management policy. Beyond this, one additional scenario

was added at the request of several Advisory Group members to address

a desire for analysis of a single harvest or timber yield level, overall and

by species that would be achievable given several altered environmental

and social constraints. To estimate these harvest levels an additional

scenario was designed that removed ―market‖ constraints, eased even-

flow constraints on several forest types, and altered three policies or

practices at once.

This scenario is based on adjustments to the Baseline scenario and

includes all of the following changes: (1) upper limit on total statewide

harvest volumes disabled, (2) upper limit on tamarack species harvest

levels disabled, (3) even-flow constraints on black spruce, elm-ash-soft

maple, maple-birch, and tamarack types disabled, (4) areal operability

limits on elm-ash-soft maple and maple-birch stands of poor site quality

disabled, (5) timber stand improvement treatments (see Table 4) were

performed on 18,750 acres annually, (6) accelerated thinning regimes

were used (see Table 5), and (7) all thinning results in stands restored to

full productivity, i.e., stands were, after treatment, indexed to the

enhanced productivity yield tables.

+444 +49 +1156 +680 +2329 This is not a ―recommended‖ harvest level, but is instead an

examination of timber yields and age-class impacts given the

scenario parameters and constraints.

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Forest Harvest Levels in Minnesota